Reinforcement Learning
A.Y. 2026/2027
Learning objectives
This course introduces the theoretical and algorithmic foundations of Reinforcement Learning, the subfield of Machine Learning studying adaptive agents that take actions and interact with an unknown environment. Reinforcement learning is a powerful paradigm for the study of autonomous AI systems, and has been applied to a wide range of tasks, including self-driving cars, game playing, customer management, and healthcare.
Expected learning outcomes
Upon completion of the course students will be able to:
- formalize problems in terms of Markov Decision Processes,
- understand basic methods of strategic exploration,
- understand algorithms for direct policy optimization,
- run experiments in simulated environments.
These objectives are measured via a combination of two components: the project report and the oral discussion. The final grade is formed by assessing the project report, and then using the oral discussion for fine tuning.
- formalize problems in terms of Markov Decision Processes,
- understand basic methods of strategic exploration,
- understand algorithms for direct policy optimization,
- run experiments in simulated environments.
These objectives are measured via a combination of two components: the project report and the oral discussion. The final grade is formed by assessing the project report, and then using the oral discussion for fine tuning.
Lesson period: Third four month period
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Third four month period
Course syllabus
Introduction
Finite horizon
Discounted horizon
Model-free RL
Temporal difference algorithms
Model-based RL
Value Function Approximation
Control using Value Function Approximation
Policy Gradient
Deep RL
Finite horizon
Discounted horizon
Model-free RL
Temporal difference algorithms
Model-based RL
Value Function Approximation
Control using Value Function Approximation
Policy Gradient
Deep RL
Prerequisites for admission
Elements of Machine Learning
Probability and Statistics
Linear Algebra
Probability and Statistics
Linear Algebra
Teaching methods
Lecture-style instruction.
Teaching Resources
The main reference material are the lecture notes provided by the lecturers.
Main reference textbook: Shie Mannor, Yishay Mansour, and Aviv Tamar. RL: Foundations. Cambridge University Press, 2026
Main reference textbook: Shie Mannor, Yishay Mansour, and Aviv Tamar. RL: Foundations. Cambridge University Press, 2026
Assessment methods and Criteria
The final grade (with a 1-30 grading range) is computed by combining the project evaluation with the result of a oral exam on the syllabus covered in class.
INF/01 - INFORMATICS - University credits: 6
Lessons: 40 hours
Professor(s)
Reception:
On appointment. The meeting will be online by first contacting the professor by email.
Online. In case of a meeting in person, Department of Computer Science, via Celoria 18 Milano, Room 7012 (7 floor)